The demand for Machine-to-Machine (M2M) and Internet of Things (IoT) communications is growing fast. The IoT is the network of physical objects or “things” embedded with electronics, software, sensors and connectivity to enable it to achieve greater value and service by exchanging data with the manufacturer, operator and/or other connected devices. Each thing is uniquely identifiable through its embedded computing system and is able to be interoperated within the existing Internet infrastructure. Typically, IoT is expected to offer advanced connectivity of devices, systems, and services that goes beyond M2M communications and covers a variety of protocols, domains, and applications. Things, in the IoT, can refer to a wide variety of devices such as heart monitoring implants, biochip transponders on farm animals, electric clams in coastal waters, automobiles with built-in sensors, or field operation devices that assist fire-fighters in search and rescue. These devices collect useful data with the help of various existing technologies and then autonomously flow the data between other devices. Current market examples include smart thermostat systems and washer/dryers that utilize WiFi for remote monitoring. IoT is expected to generate large amounts of data from diverse locations that are aggregated at a very high-velocity, thereby increasing the need to better index, store and process such data.
There is a new WiFi standard, IEEE P802.11ah D3.0, “Draft Standard for Information technology—Telecommunications and information exchange between systems Local and metropolitan area networks—Specific requirement”, that is focused on meeting this growing demand of M2M and IoT communications. This standard utilizes sub-1 GHz license-exempt bands in order to provide an extended range and potentially connectivity of up to thousands of STAs per AP. One of the use cases within this standard is directed towards sensors and meters. Such sensors and meters are of a node type defined as a non Access Point STAtion (non-AP STA) and may be deployed to monitor some type of natural activity and then report sensor measurements to an AP, either periodically or in an event-based fashion. Measuring the temperature in a certain geographical area may be an example of such monitoring activity. The temperature measurements are the sent periodically, for example hourly or daily, to an AP and may then be forwarded to some application server. In order to save energy such sensor nodes will typically be sleeping most of the time and only wake up to take a temperature sample and transmit the sample to the AP. Such sensor nodes may also often be deployed in areas where recharging or replacing the power source is difficult. Therefore it is expected that the power source lasts for several years before it needs to be replaced.
The new WiFi standard IEEE P802.11ah D3.0, hereinafter 802.11ah, introduces a new sensor STA. This is a new type of non-AP STA, using data frames with small payload size. It is also expected to have limited available power, low duty cycle and low traffic volumes. These sensor STA's are typically deployed in static locations and are defined in Section 3 “Definitions, acronyms, and abbreviations” of 802.11ah.
Since energy efficiency is a key when it comes to these sensor STA's the choice of a modulation and coding scheme (MCS) is of great importance. The 802.11ah standard does not specify how to choose the MCS. The determination of the most suitable MCS is instead done through vendor specific algorithms running in the STA. Many commonly used algorithms are variants of the Minstrel algorithm (see “Linux Kernel Wireless” http://wireless.kernel.org/en/developers/Documentation/mac80211/RateControl/minstrel). The idea with the Minstrel algorithm is to first attempt transmitting a packet with the MCS that is expected to provide the best throughput. If the package is lost, the Minstrel algorithm will choose a more robust MCS and attempt retransmission. A more robust MCS means that more bits and energy will be used in the retransmission, and that the retransmission will take longer time. This procedure, i.e. choosing a more robust MCS followed by retransmission, is repeated three times before the package is reported as lost to higher layers. The success rates of old transmissions are stored for later use when deciding which MCS to use for the next packet. The Minstrel algorithm also periodically tries random MCS's in order to estimate and determine the MCS that maximizes the throughput. The standard Minstrel algorithm, as well as many other 802.11ah link adaptation mechanisms, relies on the statistics of lost packets.
The 802.11 standard was not originally conceived to achieve power efficiency. Since sensors and other IoT devices require low power consumption, the 802.11ah amendment introduces several new features targeting increased power efficiency. One such feature is the Target Wake Time (TWT), which enables scheduling of STA's to operate at different times, in order to minimize both access contentions and the time STA's must be awake. Another power saving feature introduced in 802.11ah is the Open-Loop Link Margin Index. This feature is intended for low-duty cycle sensors, and it helps the STA to quickly estimate the MCS. As the name indicates, the Link Margin Index contains link margin information, and it is included in beacon frames or probe response frames from the AP to the STA. Yet another power saving feature in 802.11ah is to allow larger sleep periods compared to previous versions of the 802.11 standard. However, these longer sleep periods also introduce new challenges. One such challenge is that, according to 802.11, a STA that has been asleep for a long time period and now wakes up, i.e. is changing its status from DOZE to AWAKE in order to transmit, must perform a Clear Channel Assessment (CCA) until the frame sequence is detected or until a ProbeDelay time has expired. In case the STA wakes up shortly after a beacon, it may have to wait and listen during the whole Target Beacon Transmission Time (TBTT) period until the next beacon, or the whole ProbeDelay time, in order to acquire time synchronization. During this listening period power will be consumed, and thus in one way counteracts the energy savings made during the long time sleeping period. To alleviate this problem, the 802.11ah amendment introduced an AP assisted medium synchronization mechanism in order to minimize the duration of this listening period. To achieve this improved synchronization, the AP sends a short synchronization frame in the beginning of the TWT and a Random Access Window (RAW). The format of this short sync frame is of a type; No Data Packet-Clear To Send (NDP-CTS). This synchronization procedure is described in detail in Section 9.42c.1 of the 802.11ah standard.
Even if this AP assisted medium synchronization mechanism to some extent improves the energy efficiency of sensor STA's there is still room for improvements. As mentioned above there are very stringent battery life requirements for the sensor STA's. It is essential that the sensor STA, at the time of waking up, can transmit its data to the AP as power efficiently as possible. To do this, choosing the right MCS is of great importance. The Open-Loop Link Margin Index feature mentioned above addresses this problem, but it only gives information to the STA about the link budget. The information about link budget is collected at the AP based on the previous transmissions from the STA. This may be sufficient for STA's having short sleeping cycles. However, sensor STA's have long sleeping cycles and when the sensor STA wakes up, the propagation environment may have changed significantly since the last transmission. Thus, there is a major risk that the link budget information may be outdated due to changes in the transmission activity of nearby (visible and hidden) STA's.
In the 802.11ah sensor use case, it is envisioned that the communication environment is static but heavily congested and the risk of losing a packet due to interference and hidden STA's is greater than the risk of losing a packet due to poor channel conditions. However, the existing link adaptation methods, such as the Minstrel algorithm mentioned above, will reduce the MCS value when it performs a retransmission due to lost packets. If the packets are lost due to poor channel conditions this procedure guarantees a higher successful reception probability for the retransmitted packet. However, if the packet is lost because of a collision with a transmission from a contending STA this algorithm will have the opposite effect.
Using more energy during longer times will pollute the wireless environment even more. Since the packet was lost because of collision, there is at least another STA in the AP range that also wants to transmit data. Thus, if all contending STA's choose more robust MCS upon collisions, the channel utilization increases and the probability of success in the retransmission decreases. If the robustness of the MCS is further increased, this may start a vicious circle between all contending STA's and further decrease the probability of success for the retransmission. This is particularly critical in the presence of hidden STA's.